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Enregistrement W4378576107 · doi:10.1016/j.adro.2023.101275

Unanticipated Radiation Replanning for Stage III Non-small Cell Lung Cancer

2023· article· en· W4378576107 sur OpenAlexaff
Melinda Mushonga, Yee Ung, Alexander V. Louie, Patrick Cheung, Ian Poon, Liying Zhang, May Tsao

Notice bibliographique

RevueAdvances in Radiation Oncology · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueLung Cancer Diagnosis and Treatment
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMedicineLung cancerOdds ratioStage (stratigraphy)Logistic regressionRadiation therapyInternal medicineComputed tomographySurgeryNuclear medicine

Résumé

récupéré en direct d'OpenAlex

PurposeThe purpose of this study was to identify factors associated with unanticipated radiation therapy (RT) replanning in stage III non-small cell lung cancer (NSCLC).Methods and MaterialsPatients from a single institution with newly diagnosed stage III NSCLC treated with radical RT from January 1, 2016, to December 31, 2019, were retrospectively analyzed. The frequency and reasons for replanning were determined. Logistic regression analysis was used to identify factors associated with replanning.ResultsOf 144 patients included in this study, 11% (n = 16) required replanning after the start of RT. The reason for replanning in these 16 patients was changes in the target detected by cone beam computed tomography (shift in 10 patients, shrinkage in 5 patients, and growth in 1 patient). Larger planning target volume (primary and nodal) was statistically predictive of replanning (odds ratio, 2.5; 95% CI, 1.2-5.4; P = .02). The actuarial median overall survival was 33.3 months (95% CI, 10.3-43.9) for the 16 patients who were replanned and 36.3 months (95% CI, 27.4-66.5) for the remaining 128 patients (P = .96). The median time to local recurrence was 25.0 months (95% CI, 10.3-41.3) for those patients who underwent replanning, which was similar to those patients who did not undergo replanning (19.5 months; 95% CI, 11.8-23.2; P = .28).ConclusionsIn this study, 11% of patients treated with radical RT for NSCLC required replanning due to changes in the target detected by cone beam computed tomography. A larger planning target volume predicts a higher likelihood of requiring adaptive RT. Overall survival and local control were similar between patients who were replanned compared with those who were not replanned. The purpose of this study was to identify factors associated with unanticipated radiation therapy (RT) replanning in stage III non-small cell lung cancer (NSCLC). Patients from a single institution with newly diagnosed stage III NSCLC treated with radical RT from January 1, 2016, to December 31, 2019, were retrospectively analyzed. The frequency and reasons for replanning were determined. Logistic regression analysis was used to identify factors associated with replanning. Of 144 patients included in this study, 11% (n = 16) required replanning after the start of RT. The reason for replanning in these 16 patients was changes in the target detected by cone beam computed tomography (shift in 10 patients, shrinkage in 5 patients, and growth in 1 patient). Larger planning target volume (primary and nodal) was statistically predictive of replanning (odds ratio, 2.5; 95% CI, 1.2-5.4; P = .02). The actuarial median overall survival was 33.3 months (95% CI, 10.3-43.9) for the 16 patients who were replanned and 36.3 months (95% CI, 27.4-66.5) for the remaining 128 patients (P = .96). The median time to local recurrence was 25.0 months (95% CI, 10.3-41.3) for those patients who underwent replanning, which was similar to those patients who did not undergo replanning (19.5 months; 95% CI, 11.8-23.2; P = .28). In this study, 11% of patients treated with radical RT for NSCLC required replanning due to changes in the target detected by cone beam computed tomography. A larger planning target volume predicts a higher likelihood of requiring adaptive RT. Overall survival and local control were similar between patients who were replanned compared with those who were not replanned.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,541
Score d'incertitude au seuil0,535

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,402
Écart entre enseignants0,380 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2023
Routes d'admission1
Résumé présentoui

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